Application value of coaxial puncture needle (technique) in ultrasound-guided puncture biopsy of peripheral pulmonary masses.

This study aims to investigate the effect of ultrasound (US)-guided coaxial puncture needle in puncture biopsy of peripheral pulmonary masses. In this retrospective analysis, 157 patients who underwent US-guided percutaneous lung biopsy in our hospital were divided into a coaxial biopsy group and a conventional biopsy group (the control group) according to the puncture tools involved, with 73 and 84 patients, respectively. The average puncture time, number of sampling, sampling satisfaction rate, puncture success rate and complication rate between the 2 groups were compared and discussed in detail. One hundred fifty-seven patients underwent puncture biopsy, and 145 patients finally obtained definitive pathological results. The overall puncture success rate was 92.4% ([145/157]; with a puncture success rate of 97.3% [71/73] from the coaxial biopsy group and a puncture success rate of 88.1% [74/84] from the conventional biopsy group (P < .05). For peripheral pulmonary masses ≤3 cm, the average puncture time in the coaxial biopsy group was shorter than that in the conventional biopsy group, and the number of sampling, sampling satisfaction rate and puncture success rate were significantly higher than those in the conventional biopsy group (P < .05). There was no significant difference in the complication rate between the 2 groups (P > .05). For peripheral pulmonary masses >3 cm, the average puncture time in the coaxial biopsy group was still shorter than that in the conventional biopsy group (P < .05). The differences between the 2 groups in the number of sampling, satisfaction rate of the sampling, the success rate of puncture and the incidence of complications were not significant (P > .05). US guided coaxial puncture biopsy could save puncture time, increase the number of sampling, and improve the satisfaction rate of sampling and the success rate of puncture (especially for small lesions) by establishing a biopsy channel on the basis of the coaxial needle sheath. It provided reliable information for the diagnosis, differential diagnosis and individualized accurate treatment of lesions as well.

Diagnostic value of artificial intelligence automatic detection systems for breast BI-RADS 4 nodules.

BACKGROUND: The incidence rate of breast cancer has exceeded that of lung cancer, and it has become the most malignant type of cancer in the world. BI-RADS 4 breast nodules have a wide range of malignant risks and are associated with challenging clinical decision-making. AIM: To explore the diagnostic value of artificial intelligence (AI) automatic detection systems for BI-RADS 4 breast nodules and to assess whether conventional ultrasound BI-RADS classification with AI automatic detection systems can reduce the probability of BI-RADS 4 biopsy. METHODS: A total of 107 BI-RADS breast nodules confirmed by pathology were selected between June 2019 and July 2020 at Hwa Mei Hospital, University of Chinese Academy of Sciences. These nodules were classified by ultrasound doctors and the AI-SONIC breast system. The diagnostic values of conventional ultrasound, the AI automatic detection system, conventional ultrasound combined with the AI automatic detection system and adjusted BI-RADS classification diagnosis were statistically analyzed. RESULTS: Among the 107 breast nodules, 61 were benign (57.01%), and 46 were malignant (42.99%). The pathology results were considered the gold standard; furthermore, the sensitivity, specificity, accuracy, Youden index, and positive and negative predictive values were 84.78%, 67.21%, 74.77%, 0.5199, 66.10% and 85.42% for conventional ultrasound BI-RADS classification diagnosis, 86.96%, 75.41%, 80.37%, 0.6237, 72.73%, and 88.46% for automatic AI detection, 80.43%, 90.16%, 85.98%, 0.7059, 86.05%, and 85.94% for conventional ultrasound BI-RADS classification with automatic AI detection and 93.48%, 67.21%, 78.50%, 0.6069, 68.25%, and 93.18% for adjusted BI-RADS classification, respectively. The biopsy rate, cancer detection rate and malignancy risk were 100%, 42.99% and 0% and 67.29%, 61.11%, and 1.87% before and after BI-RADS adjustment, respectively. CONCLUSION: Automatic AI detection has high accuracy in determining benign and malignant BI-RADS 4 breast nodules. Conventional ultrasound BI-RADS classification combined with AI automatic detection can reduce the biopsy rate of BI-RADS 4 breast nodules.